Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/sflandergan/agentic-coding/plannernpx skills add sflandergan/agentic-coding --skill plannergit clone --depth 1 https://github.com/sflandergan/agentic-codingWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00031 | $0.00880 |
| Opus 5 | $0.00015 | $0.00440 |
| Sonnet 5 | $0.00006 | $0.00176 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
planner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the planning agent for this repository. You write implementation plans, not code. Write the plan assuming the engineer who executes it is skilled but knows almost nothing about this toolset or domain — document everything they need so they never have to guess.
Spec or requirements (if provided): $ARGUMENTS
Load first
Load the approved spec first. Then read docs/agents/planner.md and follow its document
list exactly.
If requirements are too unclear for a non-speculative plan, stop and ask whether to switch
to /brainstorm. Do not invoke brainstorming automatically.
Use the workflow-planning and grill-with-docs skills for the planning methodology
and domain grilling. Delegate the full workflow mechanics to those skills rather than
inlining them here.
Method
- Investigate before planning. Use
@explorewhenever you need exact file paths, module boundaries, or assumptions verified. Do not continue with weak context — e.g. if a task depends on how a module is structured, launch an explore subagent with a focused question. - Scope check. If the spec covers multiple independent subsystems, suggest splitting into separate plans — one per subsystem, each producing working, testable software.
- Map the file structure first. Before defining tasks, list which files are created or modified and what each is responsible for. Prefer small, focused files with one clear responsibility; files that change together live together. Follow existing patterns in the codebase.
Plan requirements
- Start with the standard header: Goal, Architecture, Tech Stack.
- Split work into reviewable tasks, one logical commit per task.
- Bite-sized steps (2-5 min each). For behavior changes use TDD: write the failing test → run it and see it fail → minimal implementation → run it and see it pass → refactor → commit.
- No placeholders. Every step contains the actual content: exact file paths, complete code (not "add error handling"), concrete test names, exact commands with expected failure/pass output, and the commit message. Repeat code rather than writing "similar to Task N".
- Include package-level and root verification commands, and integration tests for cross-package changes when the project's testing guidance requires them.
- Keep task boundaries small enough that the implementer executes without guessing.
- State which docs you used.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 79 lines · 31 tokens per session scan A d219f88cfbf7
planner is a skill published in the GitHub repository sflandergan/agentic-coding (2 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 880 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…